Development of a Mathematical Model and Information System for Forecasting Intercity Trip Demands for the Taipei, Taiwan Metropolitan Area PDF Download

Author: David Tong Yong Lee
Publisher:
ISBN:
Category : Transportation
Languages : en
Pages : 294

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Author: David Tong Yong Lee
Publisher:
ISBN:
Category : Transportation
Languages : en
Pages : 296

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Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 954

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Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 936

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Author:
Publisher:
ISBN:
Category : Asia
Languages : en
Pages : 424

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Author: Frank Joseph Shulman
Publisher: Seattle : University of Washington Press
ISBN:
Category : Education
Languages : en
Pages : 360

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Annotated bibliography of doctoral thesis material in western languages on China - lists publications on historical and legal aspects, political aspects, international relations, economics, education, social and cultural anthropology, fine arts, architecture, geographical aspects, language, religion and philosophy, Chinese immigrant communities in other countries, etc.

Author: Peng Yue
Publisher:
ISBN:
Category : Trip generation
Languages : en
Pages : 142

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In order to automate the travel demand forecasting process in urban transportation planning, a number of commercial computer based travel demand forecasting models have been developed, which have provided transportation planners with powerful and flexible tools in modeling a traffic network for planning or traffic impact studies. It is commonly recognized that none of the existing travel demand forecasting software is perfectly suited for all application network scenarios and traffic conditions. A particular model, which is strong in one application scenario, may be weak in a different application scenario. This report intends to present a comparative study of two widely used computer based travel demand forecasting models: QRS II vs. EMME/2. The comparative study is designed to identify main features and differences of the two models, with an attempt to provide some useful information to practitioners. The comparative description of basic features of two models in this report includes model structure, network development, data input, network modification, parameter calibration, and modeling output. In the comparison of advanced features, the "calculate" function in QRS II and macro language in EMME/2 are presented. A real-world small urban network, south Missouri City Network, is used to support the comparison effort. The study has found that both QRS II and EMME/2 models are reliable to model real-world networks. However, QRS II is relatively easy to use for inexperienced users because of its comprehensive default parameters, calculation formulas, procedures and the embedded four-step travel demand forecasting process. On the other hand, EMME/2 provides more powerful and flexible modules for users to perform more complex tasks

Author: Michael G. McNally
Publisher:
ISBN:
Category : Geographic information systems
Languages : en
Pages : 48

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Author: Cambridge Systematics
Publisher:
ISBN:
Category : Traffic estimation
Languages : en
Pages :

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The purpose of the manual is to provide an introduction to travel forecasting to enable transportation planners and analysts to utilize the UMTA Transportation Planning System (UTPS) effectively.

Author: Zhanhong Cheng
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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"The metro is indispensable for the urban transportation system. As the world enters the era of informatization and digitalization, data generated from smart card fare collection systems (smart card data) have played an important role in the planning and operation of metro systems. A large body of research uses smart card data to understand passenger travel behavior in metro systems; it has been found that individual mobility in metro systems is highly regular with interpretable patterns. Besides, smart card data have also been extensively used in assisting the operation and control of metro systems, such as inferring trip origins/destinations and forecasting passenger demand. However, a lack in existing research is the connection between the above two aspects---how to use the unique travel behavior characteristics of metro passengers to establish better data-driven applications. To fill this gap, this thesis aims to develop travel-behavior-based inference and forecasting models in metro systems. The three contributions of this thesis, enclosed in three scientific papers, are (1) trip destination inference, (2) real-time boarding demand forecasting, and (3) real-time origin-destination (OD) matrices forecasting. All models developed in the thesis are tested by real-world smart card data from Guangzhou, China. First, this thesis develops a probabilistic topic model to infer trip destination from tap-in only smart card system. The probabilistic topic model is learned from passengers' historical travel behavior and can predict the most likely destination of a trip given the origin and the departure time. Complementing existing trip-chain-based destination inference methods, the proposed model is particularly useful for isolated trips where conventional methods fail. Besides destination inference, latent topics learned by the probabilistic model can be used to analyze passengers' travel behavior patterns. Second, this thesis aims to incorporate travel behavior regularity into passenger boarding demand/flow forecasting. Utilizing the strong regularity rooted in individuals' travel behavior, a new concept named ``returning flow'' is proposed to capture the generative mechanism of boarding flow. The returning flow is highly correlated to the boarding flow and can be used as a covariate in a time series model to improve the boarding flow forecasting. Extensive experiments show the effectiveness of using the travel behavioral feature boarding flow forecasting. The Last part of this thesis addresses the real-time OD matrices forecasting problem in metro systems. Using the low-rank property of OD data, the forecasting is formulated into a low-rank vector autoregression (VAR) problem and is solved by dynamic mode decomposition (DMD). Next, a forgetting ratio is introduced to exponentially reduce the weights for historical data. Moreover, an online update algorithm is developed to update the model efficiently without storing historical data or retraining. Experiments show the proposed model significantly outperforms baseline models in forecasting both OD matrices and boarding flow. In summary, this thesis uses travel behavioral characteristics to improve inference and forecasting models in metro systems. The proposed models and solutions are beneficial to the intelligent operation of metro systems. The three tasks of destination inference, boarding flow forecasting, and OD matrices forecasting correspond to individual-level, station-level, and-network level applications, respectively. By these three levels, this thesis demonstrates the considerable potential of using travel behavior in various metro applications"--